Neural Predictor-Based Dynamic Surface Predictive Control for Power Converters

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of explosion of complexity inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter.

Automated summary

In this letter, a neural predictor-based dynamic surface predictive control framework is proposed, which combines the advantages of adaptive dynamic surface control and finite control-set model predictive control. The estimation of neural predictor is used to identify the system dynamics and unknown uncertainties, avoiding the issue of complexity explosion in classical back-stepping control. The proposed solution explicitly deals with model uncertainties and disturbances, leading to a simpler adaptive predictive control solution.